Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data

Abstract

Hurricanes are costly natural disasters periodically faced by households in coastal and, to some extent, inland areas. Public agencies must understand household behavior to develop evacuation plans that align with evacuee choices and behavior. This paper presents a previously unknown household-level hurricane evacuation destination type choice model. The discrete choice of destination type is modeled using a nested logit model. Although previous literature considers only houses of friends and relatives and hotels for modeling purposes, this paper incorporates public shelters, churches, and an aggregated destination type denoted other. This research found that the variables influencing this choice include hurricane position at evacuation time, household geographic location, race, income, preparation time, changes in evacuation plans, previous experiences with major hurricanes, household members working during the evacuation, and evacuation notices. The findings of this paper are useful to understand the competition among destination types and how the characteristics of the demand can be used to develop evacuation strategies, such as increasing and/or decreasing use of public shelters, and measuring the effect of evacuation notices in areas with high accessibility to hotels.

A household-level flood evacuation decision model in Quezon City, Philippines

“…Extensive application of discrete choice models in many disciplines exists in the literature. Examples of these are in the field of social sciences (Lewis and Noguchi 2009), medicine (Kwak and Matthews 2002;Tay et al 2009), econometrics (Pryanishnikov andZigova 2003;Zaghdoudi 2013), transportation (Scott and Kanaroglou 2002;Fujiwara and Zhang 2005;Zhang et al 2009), and evacuation modeling (e.g., Charnkol et al 2007;Mesa-Arango et al 2013;Sadri et al 2014a). One form of discrete choice model is logit, as detailed in Hosmer and Lemeshow (2000) and Train (2009).…”

“…The second stage is the evacuation distribution of which the origin-destination is either assumed using the potential locations of shelters or estimated from the destination choices of evacuees gathered from past evacuation events (e.g., Mesa-Arango et al 2013). The third stage is the mode split which specifies the type of mode taken by evacuees.…”

Numerical Methods for Reliability and Safety Assessment

“…Numerous researchers addressed the destination choice models for hurricane hazards (Mesa-Arango et al 2013;Hasan et al 2010;Dash and Gladwin 2007;Wilmot 2004, Lindell et al 2005). The age of the decision maker, presence of children or elderly persons in the household, gender, disability, race and ethnicity, income, previous experience, household size, living in a single-family dwelling unit, risk perception, geographic characteristics and location (proximity to highways and exit routes) play important roles in the evacuation decision making process (Dash and Gladwin 2007;Lindell et al 2005;Gladwin and Peacock 1997).…”

“…The importance of evacuation studies in the aftermath of a disaster is recognized by the natural hazards researchers (Tamima and Chouinard 2012;Hasan et al 2010;Mesa-Arango et al 2013). Disasters result in displaced households and the estimation for the demand for shelters is a part of disaster preparedness studies.…”

Possible Factors Promoting Car Evacuation in the 2011 Tohoku Tsunami Revealed by Analysing a Large-Scale Questionnaire Survey in Kesennuma City

“…Even for the 2011 event, many studies and surveys have been limited to general features of evacuation behaviour [12][13][14], and few studies have attempted to analyse the decision-making process of shelter-choice behaviour in detail [15,16]. Because mega-tsunami disasters are low-frequency disasters and it is challenging to obtain sufficient data, it is challenging to study comprehensive decision-making processes, such as evacuation departures [17], destination choice [18], mode choice [19], and route choice [20], as are studied in the case of typhoon disasters. Thus, we must thoroughly investigate the decision-making processes that are associated with tsunami disasters.…”